class_docstr: Interpolation upsample module in decoder for UNet.
  
  This module uses interpolation to upsample feature map in the decoder
  of UNet. It consists of one interpolation upsample layer and one
  convolutional layer. It can be one interpolation upsample layer followed
  by one convolutional layer (conv_first=False) or one convolutional layer
  followed by one interpolation upsample layer (conv_first=True).
  
  Args:
      in_channels (int): Number of input channels.
      out_channels (int): Number of output channels.
      with_cp (bool): Use checkpoint or not. Using checkpoint will save some
          memory while slowing down the training speed. Default: False.
      norm_cfg (dict | None): Config dict for normalization layer.
          Default: dict(type='BN').
      act_cfg (dict | None): Config dict for activation layer in ConvModule.
          Default: dict(type='ReLU').
      conv_cfg (dict | None): Config dict for convolution layer.
          Default: None.
      conv_first (bool): Whether convolutional layer or interpolation
          upsample layer first. Default: False. It means interpolation
          upsample layer followed by one convolutional layer.
      kernel_size (int): Kernel size of the convolutional layer. Default: 1.
      stride (int): Stride of the convolutional layer. Default: 1.
      padding (int): Padding of the convolutional layer. Default: 1.
      upsample_cfg (dict): Interpolation config of the upsample layer.
          Default: dict(
              scale_factor=2, mode='bilinear', align_corners=False).
docstr: Forward function.
docstr_tok: ["Forward","function","."]
code: def forward(self, x):
      """Forward function."""
  
      if self.with_cp and x.requires_grad:
          out = cp.checkpoint(self.interp_upsample, x)
      else:
          out = self.interp_upsample(x)
      return out
  
imports: import torch
  import torch.nn
  import torch.utils.checkpoint
  from mmcv.cnn import ConvModule, build_norm_layer
  from mmengine.model import BaseModule
  from mmengine.utils.dl_utils.parrots_wrapper import _BatchNorm
  from mmocr.registry import MODELS
label_desc: Use image node_id 2 for calling the InterpConv obj's underlying member method code with example usage: obj.forward(x) and returns: out